Annotations#
Show imports
%load_ext autoreload
%autoreload 2
import os
import dimcat as dc
import ms3
import plotly.express as px
from dimcat import groupers, plotting
import utils
Show source
RESULTS_PATH = os.path.abspath(os.path.join(utils.OUTPUT_FOLDER, "overview"))
os.makedirs(RESULTS_PATH, exist_ok=True)
def make_output_path(
filename: str,
extension=None,
path=RESULTS_PATH,
) -> str:
return utils.make_output_path(filename=filename, extension=extension, path=path)
def save_figure_as(
fig, filename, formats=("png", "pdf"), directory=RESULTS_PATH, **kwargs
):
if formats is not None:
for fmt in formats:
plotting.write_image(fig, filename, directory, format=fmt, **kwargs)
else:
plotting.write_image(fig, filename, directory, **kwargs)
Loading data
Show source
D = utils.get_dataset("poulenc_mouvements_perpetuels", corpus_release="v2.4")
package = D.inputs.get_package()
package_info = package._package.custom
git_tag = package_info.get("git_tag")
utils.print_heading("Data and software versions")
print("Francis Poulenc – Mouvements Perpetuels version v2.4")
print(f"Datapackage '{package.package_name}' @ {git_tag}")
print(f"dimcat version {dc.__version__}\n")
D
Data and software versions
--------------------------
Francis Poulenc – Mouvements Perpetuels version v2.4
Datapackage 'poulenc_mouvements_perpetuels' @ v2.4
dimcat version 3.4.0
Dataset
=======
{'inputs': {'basepath': None,
'packages': {'poulenc_mouvements_perpetuels': ["'poulenc_mouvements_perpetuels.measures' "
'(MuseScoreFacetName.MuseScoreMeasures)',
"'poulenc_mouvements_perpetuels.notes' "
'(MuseScoreFacetName.MuseScoreNotes)',
"'poulenc_mouvements_perpetuels.expanded' "
'(MuseScoreFacetName.MuseScoreHarmonies)',
"'poulenc_mouvements_perpetuels.chords' "
'(MuseScoreFacetName.MuseScoreChords)',
"'poulenc_mouvements_perpetuels.metadata' "
'(FeatureName.Metadata)']}},
'outputs': {'basepath': None, 'packages': {}},
'pipeline': []}
filtered_D = D.apply_step("HasHarmonyLabelsFilter")
all_metadata = filtered_D.get_metadata()
assert len(all_metadata) > 0, "No pieces selected for analysis."
chronological_corpus_names = all_metadata.get_corpus_names()
DCML harmony labels#
Show source
all_annotations = filtered_D.get_feature("DcmlAnnotations")
is_annotated_mask = all_metadata.label_count > 0
is_annotated_index = all_metadata.index[is_annotated_mask]
annotated_notes = filtered_D.get_feature("notes").subselect(is_annotated_index)
print(f"The annotated pieces have {len(annotated_notes)} notes.")
The annotated pieces have 1563 notes.
all_chords = filtered_D.get_feature("harmonylabels")
print(
f"{len(all_annotations)} annotations, of which {len(all_chords)} are harmony labels."
)
277 annotations, of which 268 are harmony labels.
Harmony labels#
Unigrams#
For computing unigram statistics, the tokens need to be grouped by their occurrence within a major or a minor key
because this changes their meaning. To that aim, the annotated corpus needs to be sliced into contiguous localkey
segments which are then grouped into a major (is_minor=False) and a minor group.
root_durations = (
all_chords[all_chords.root.between(-5, 6)]
.groupby(["root", "chord_type"])
.duration_qb.sum()
)
# sort by stacked bar length:
# root_durations = root_durations.sort_values(key=lambda S: S.index.get_level_values(0).map(S.groupby(level=0).sum()),
# ascending=False)
bar_data = root_durations.reset_index()
bar_data.root = bar_data.root.map(ms3.fifths2iv)
fig = px.bar(
bar_data,
x="root",
y="duration_qb",
color="chord_type",
title="Distribution of chord types over chord roots",
labels=dict(
root="Chord root expressed as interval above the local (or secondary) tonic",
duration_qb="duration in quarter notes",
chord_type="chord type",
),
)
fig.update_layout(**utils.STD_LAYOUT)
save_figure_as(fig, "chord_type_distribution_over_scale_degrees_absolute_stacked_bars")
fig.show()
relative_roots = all_chords[
["numeral", "duration_qb", "relativeroot", "localkey_is_minor", "chord_type"]
].copy()
relative_roots["relativeroot_resolved"] = ms3.transform(
relative_roots, ms3.resolve_relative_keys, ["relativeroot", "localkey_is_minor"]
)
has_rel = relative_roots.relativeroot_resolved.notna()
relative_roots.loc[has_rel, "localkey_is_minor"] = relative_roots.loc[
has_rel, "relativeroot_resolved"
].str.islower()
relative_roots["root"] = ms3.transform(
relative_roots, ms3.roman_numeral2fifths, ["numeral", "localkey_is_minor"]
)
chord_type_frequency = all_chords.chord_type.value_counts()
replace_rare = ms3.map_dict(
{t: "other" for t in chord_type_frequency[chord_type_frequency < 500].index}
)
relative_roots["type_reduced"] = relative_roots.chord_type.map(replace_rare)
# is_special = relative_roots.chord_type.isin(('It', 'Ger', 'Fr'))
# relative_roots.loc[is_special, 'root'] = -4
root_durations = (
relative_roots.groupby(["root", "type_reduced"])
.duration_qb.sum()
.sort_values(ascending=False)
)
bar_data = root_durations.reset_index()
bar_data.root = bar_data.root.map(ms3.fifths2iv)
root_order = (
bar_data.groupby("root")
.duration_qb.sum()
.sort_values(ascending=False)
.index.to_list()
)
fig = px.bar(
bar_data,
x="root",
y="duration_qb",
color="type_reduced",
barmode="group",
log_y=True,
color_discrete_map=utils.TYPE_COLORS,
category_orders=dict(
root=root_order,
type_reduced=relative_roots.type_reduced.value_counts().index.to_list(),
),
labels=dict(
root="intervallic difference between chord root to the local or secondary tonic",
duration_qb="duration in quarter notes",
type_reduced="chord type",
),
width=1000,
height=400,
)
fig.update_layout(
**utils.STD_LAYOUT,
legend=dict(
orientation="h",
xanchor="right",
x=1,
y=1,
),
)
save_figure_as(fig, "chord_type_distribution_over_scale_degrees_absolute_grouped_bars")
fig.show()
print(
f"Reduced to {len(set(bar_data.iloc[:,:2].itertuples(index=False, name=None)))} types. "
f"Paper cites the sum of types in major and types in minor (see below), treating them as distinct."
)
Reduced to 6 types. Paper cites the sum of types in major and types in minor (see below), treating them as distinct.
dim_or_aug = bar_data[
bar_data.root.str.startswith("a") | bar_data.root.str.startswith("d")
].duration_qb.sum()
complete = bar_data.duration_qb.sum()
print(
f"On diminished or augmented scale degrees: {dim_or_aug} / {complete} = {dim_or_aug / complete}"
)
On diminished or augmented scale degrees: 0.0 / 313.875 = 0.0
chords_by_mode = groupers.ModeGrouper().process(all_chords)
chords_by_mode.format = "scale_degree"
Whole dataset#
unigram_proportions = chords_by_mode.get_default_analysis()
unigram_proportions.make_ranking_table()
| mode | major | minor | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| chord_and_mode | scale_degrees | duration_qb | proportion | proportion_% | chord_and_mode | scale_degrees | duration_qb | proportion | proportion_% | |
| rank | ||||||||||
| 1 | ii7(9), major | (2, 4, 6, 1) | 20.5 | 0.080550 | 8.06 % | iv(96), minor | (4, 6, 2) | 7.000 | 0.117895 | 11.79 % |
| 2 | I, major | (1, 3, 5) | 18.0 | 0.070727 | 7.07 % | i2, minor | (7, 1, 3, 5) | 5.000 | 0.084211 | 8.42 % |
| 3 | iii(b9), major | (3, 5, 7) | 16.0 | 0.062868 | 6.29 % | i, minor | (1, 3, 5) | 5.000 | 0.084211 | 8.42 % |
| 4 | IVM2, major | (3, 4, 6, 1) | 14.0 | 0.055010 | 5.5 % | i64(13), minor | (5, 1, 3) | 4.000 | 0.067368 | 6.74 % |
| 5 | IM7, major | (1, 3, 5, 7) | 13.0 | 0.051081 | 5.11 % | v, minor | (5, 7, 2) | 4.000 | 0.067368 | 6.74 % |
| 6 | V(^2), major | (5, 6, 2) | 11.0 | 0.043222 | 4.32 % | ii%65, minor | (4, 6, 1, 2) | 4.000 | 0.067368 | 6.74 % |
| 7 | V7(^2), major | (5, 6, 2, 4) | 9.0 | 0.035363 | 3.54 % | i(9b6), minor | (1, 3, b6) | 4.000 | 0.067368 | 6.74 % |
| 8 | V(96), major | (5, 7, 3) | 8.0 | 0.031434 | 3.14 % | ii%43, minor | (6, 1, 2, 4) | 4.000 | 0.067368 | 6.74 % |
| 9 | ii7, major | (2, 4, 6, 1) | 8.0 | 0.031434 | 3.14 % | i43, minor | (5, 7, 1, 3) | 3.000 | 0.050526 | 5.05 % |
| 10 | V(6), major | (5, 7, 3) | 7.0 | 0.027505 | 2.75 % | V7(9b6)/III, minor | (7, 2, b5, 6) | 2.375 | 0.040000 | 4.0 % |
| 11 | ii, major | (2, 4, 6) | 6.5 | 0.025540 | 2.55 % | iio6, minor | (4, 6, 2) | 2.000 | 0.033684 | 3.37 % |
| 12 | IM7(9), major | (1, 3, 5, 7) | 6.0 | 0.023576 | 2.36 % | V(9)/III, minor | (7, 2, 4) | 2.000 | 0.033684 | 3.37 % |
| 13 | i7, major | (1, b3, 5, b7) | 6.0 | 0.023576 | 2.36 % | V(96)/III, minor | (7, 2, 5) | 2.000 | 0.033684 | 3.37 % |
| 14 | V(6+4), major | (5, 7, 3) | 6.0 | 0.023576 | 2.36 % | i(96), minor | (1, 3, 6) | 2.000 | 0.033684 | 3.37 % |
| 15 | v(119), major | (5, b7, 2) | 5.0 | 0.019646 | 1.96 % | i(9), minor | (1, 3, 5) | 2.000 | 0.033684 | 3.37 % |
| 16 | V7(96), major | (5, 7, 3, 4) | 4.5 | 0.017682 | 1.77 % | i(6), minor | (1, 3, 6) | 2.000 | 0.033684 | 3.37 % |
| 17 | ii65(9), major | (4, 6, 1, 2) | 4.0 | 0.015717 | 1.57 % | V7/iv, minor | (1, #3, 5, 7) | 2.000 | 0.033684 | 3.37 % |
| 18 | v7(b62), major | (6, b7, b3, 4) | 4.0 | 0.015717 | 1.57 % | v6, minor | (7, 2, 5) | 2.000 | 0.033684 | 3.37 % |
| 19 | V, major | (5, 7, 2) | 4.0 | 0.015717 | 1.57 % | iv, minor | (4, 6, 1) | 1.000 | 0.016842 | 1.68 % |
| 20 | V7(+964), major | (5, 1, 3, 4) | 4.0 | 0.015717 | 1.57 % | NaN | NaN | NaN | NaN | NaN |
| 21 | V7(9^2), major | (5, 6, 2, 4) | 4.0 | 0.015717 | 1.57 % | NaN | NaN | NaN | NaN | NaN |
| 22 | iM7(^4), major | (1, b3, 4, 7) | 4.0 | 0.015717 | 1.57 % | NaN | NaN | NaN | NaN | NaN |
| 23 | IV, major | (4, 6, 1) | 4.0 | 0.015717 | 1.57 % | NaN | NaN | NaN | NaN | NaN |
| 24 | I+7(#9), major | (1, 3, #5, b7) | 4.0 | 0.015717 | 1.57 % | NaN | NaN | NaN | NaN | NaN |
| 25 | V7/V, major | (2, #4, 6, 1) | 3.5 | 0.013752 | 1.38 % | NaN | NaN | NaN | NaN | NaN |
| 26 | ii(9), major | (2, 4, 6) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 27 | V7(9)/V, major | (2, #4, 6, 1) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 28 | i, major | (1, b3, 5) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 29 | ii(139), major | (2, 4, 6) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 30 | ii6(119), major | (4, 6, 2) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 31 | V(6^2), major | (5, 6, 3) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 32 | V(64), major | (5, 1, 3) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 33 | IVM65, major | (6, 1, 3, 4) | 2.5 | 0.009823 | 0.98 % | NaN | NaN | NaN | NaN | NaN |
| 34 | V7, major | (5, 7, 2, 4) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 35 | VM7(^2), major | (5, 6, 2, #4) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 36 | iM7(4), major | (1, 4, 5, 7) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 37 | iM7, major | (1, b3, 5, 7) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 38 | iii7(b9), major | (3, 5, 7, 2) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 39 | ii(64+2), major | (2, 5, 7) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 40 | v(b96^2), major | (5, 6, 3) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 41 | v(#119^2), major | (5, 6, 2) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 42 | V7(6), major | (5, 7, 3, 4) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 43 | V(b6^2), major | (5, 6, b3) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 44 | V(b139), major | (5, 7, 2) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 45 | ii7(+2), major | (2, 4, 6, 1) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 46 | ii(94), major | (2, 5, 6) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 47 | IM7(6), major | (1, 3, 6, 7) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 48 | ii(13), major | (2, 4, 6) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 49 | V7(964), major | (5, 1, 3, 4) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 50 | i(b13), major | (1, b3, 5) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 51 | ii2, major | (1, 2, 4, 6) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 52 | I(^2), major | (1, 2, 5) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 53 | iii64, major | (7, 3, 5) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 54 | ii64(9), major | (6, 2, 4) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
| 55 | iii, major | (3, 5, 7) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
| 56 | iii43/V, major | (#4, 6, 7, 2) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
| 57 | IV64(9), major | (1, 4, 6) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
| 58 | I64(9), major | (5, 1, 3) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
| 59 | vi, major | (6, 1, 3) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
chords_by_mode.apply_step("Counter")
| count | |||||
|---|---|---|---|---|---|
| mode | corpus | piece | chord_and_mode | scale_degrees | |
| major | poulenc_mouvements_perpetuels | 01_assez_modere | IM7, major | (1, 3, 5, 7) | 13 |
| I, major | (1, 3, 5) | 12 | |||
| V(^2), major | (5, 6, 2) | 11 | |||
| V7(^2), major | (5, 6, 2, 4) | 9 | |||
| i7, major | (1, b3, 5, b7) | 6 | |||
| ... | ... | ... | ... | ... | ... |
| minor | poulenc_mouvements_perpetuels | 02_tres_modere | i(96), minor | (1, 3, 6) | 2 |
| iio6, minor | (4, 6, 2) | 2 | |||
| v6, minor | (7, 2, 5) | 2 | |||
| iv, minor | (4, 6, 1) | 1 | |||
| V7(9b6)/III, minor | (7, 2, b5, 6) | 1 |
79 rows × 1 columns
chords_by_mode.format = "scale_degree"
chords_by_mode.get_default_analysis().make_ranking_table()
| mode | major | minor | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| chord_and_mode | scale_degrees | duration_qb | proportion | proportion_% | chord_and_mode | scale_degrees | duration_qb | proportion | proportion_% | |
| rank | ||||||||||
| 1 | ii7(9), major | (2, 4, 6, 1) | 20.5 | 0.080550 | 8.06 % | iv(96), minor | (4, 6, 2) | 7.000 | 0.117895 | 11.79 % |
| 2 | I, major | (1, 3, 5) | 18.0 | 0.070727 | 7.07 % | i2, minor | (7, 1, 3, 5) | 5.000 | 0.084211 | 8.42 % |
| 3 | iii(b9), major | (3, 5, 7) | 16.0 | 0.062868 | 6.29 % | i, minor | (1, 3, 5) | 5.000 | 0.084211 | 8.42 % |
| 4 | IVM2, major | (3, 4, 6, 1) | 14.0 | 0.055010 | 5.5 % | i64(13), minor | (5, 1, 3) | 4.000 | 0.067368 | 6.74 % |
| 5 | IM7, major | (1, 3, 5, 7) | 13.0 | 0.051081 | 5.11 % | v, minor | (5, 7, 2) | 4.000 | 0.067368 | 6.74 % |
| 6 | V(^2), major | (5, 6, 2) | 11.0 | 0.043222 | 4.32 % | ii%65, minor | (4, 6, 1, 2) | 4.000 | 0.067368 | 6.74 % |
| 7 | V7(^2), major | (5, 6, 2, 4) | 9.0 | 0.035363 | 3.54 % | i(9b6), minor | (1, 3, b6) | 4.000 | 0.067368 | 6.74 % |
| 8 | V(96), major | (5, 7, 3) | 8.0 | 0.031434 | 3.14 % | ii%43, minor | (6, 1, 2, 4) | 4.000 | 0.067368 | 6.74 % |
| 9 | ii7, major | (2, 4, 6, 1) | 8.0 | 0.031434 | 3.14 % | i43, minor | (5, 7, 1, 3) | 3.000 | 0.050526 | 5.05 % |
| 10 | V(6), major | (5, 7, 3) | 7.0 | 0.027505 | 2.75 % | V7(9b6)/III, minor | (7, 2, b5, 6) | 2.375 | 0.040000 | 4.0 % |
| 11 | ii, major | (2, 4, 6) | 6.5 | 0.025540 | 2.55 % | iio6, minor | (4, 6, 2) | 2.000 | 0.033684 | 3.37 % |
| 12 | IM7(9), major | (1, 3, 5, 7) | 6.0 | 0.023576 | 2.36 % | V(9)/III, minor | (7, 2, 4) | 2.000 | 0.033684 | 3.37 % |
| 13 | i7, major | (1, b3, 5, b7) | 6.0 | 0.023576 | 2.36 % | V(96)/III, minor | (7, 2, 5) | 2.000 | 0.033684 | 3.37 % |
| 14 | V(6+4), major | (5, 7, 3) | 6.0 | 0.023576 | 2.36 % | i(96), minor | (1, 3, 6) | 2.000 | 0.033684 | 3.37 % |
| 15 | v(119), major | (5, b7, 2) | 5.0 | 0.019646 | 1.96 % | i(9), minor | (1, 3, 5) | 2.000 | 0.033684 | 3.37 % |
| 16 | V7(96), major | (5, 7, 3, 4) | 4.5 | 0.017682 | 1.77 % | i(6), minor | (1, 3, 6) | 2.000 | 0.033684 | 3.37 % |
| 17 | ii65(9), major | (4, 6, 1, 2) | 4.0 | 0.015717 | 1.57 % | V7/iv, minor | (1, #3, 5, 7) | 2.000 | 0.033684 | 3.37 % |
| 18 | v7(b62), major | (6, b7, b3, 4) | 4.0 | 0.015717 | 1.57 % | v6, minor | (7, 2, 5) | 2.000 | 0.033684 | 3.37 % |
| 19 | V, major | (5, 7, 2) | 4.0 | 0.015717 | 1.57 % | iv, minor | (4, 6, 1) | 1.000 | 0.016842 | 1.68 % |
| 20 | V7(+964), major | (5, 1, 3, 4) | 4.0 | 0.015717 | 1.57 % | NaN | NaN | NaN | NaN | NaN |
| 21 | V7(9^2), major | (5, 6, 2, 4) | 4.0 | 0.015717 | 1.57 % | NaN | NaN | NaN | NaN | NaN |
| 22 | iM7(^4), major | (1, b3, 4, 7) | 4.0 | 0.015717 | 1.57 % | NaN | NaN | NaN | NaN | NaN |
| 23 | IV, major | (4, 6, 1) | 4.0 | 0.015717 | 1.57 % | NaN | NaN | NaN | NaN | NaN |
| 24 | I+7(#9), major | (1, 3, #5, b7) | 4.0 | 0.015717 | 1.57 % | NaN | NaN | NaN | NaN | NaN |
| 25 | V7/V, major | (2, #4, 6, 1) | 3.5 | 0.013752 | 1.38 % | NaN | NaN | NaN | NaN | NaN |
| 26 | ii(9), major | (2, 4, 6) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 27 | V7(9)/V, major | (2, #4, 6, 1) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 28 | i, major | (1, b3, 5) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 29 | ii(139), major | (2, 4, 6) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 30 | ii6(119), major | (4, 6, 2) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 31 | V(6^2), major | (5, 6, 3) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 32 | V(64), major | (5, 1, 3) | 3.0 | 0.011788 | 1.18 % | NaN | NaN | NaN | NaN | NaN |
| 33 | IVM65, major | (6, 1, 3, 4) | 2.5 | 0.009823 | 0.98 % | NaN | NaN | NaN | NaN | NaN |
| 34 | V7, major | (5, 7, 2, 4) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 35 | VM7(^2), major | (5, 6, 2, #4) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 36 | iM7(4), major | (1, 4, 5, 7) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 37 | iM7, major | (1, b3, 5, 7) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 38 | iii7(b9), major | (3, 5, 7, 2) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 39 | ii(64+2), major | (2, 5, 7) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 40 | v(b96^2), major | (5, 6, 3) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 41 | v(#119^2), major | (5, 6, 2) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 42 | V7(6), major | (5, 7, 3, 4) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 43 | V(b6^2), major | (5, 6, b3) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 44 | V(b139), major | (5, 7, 2) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 45 | ii7(+2), major | (2, 4, 6, 1) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 46 | ii(94), major | (2, 5, 6) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 47 | IM7(6), major | (1, 3, 6, 7) | 2.0 | 0.007859 | 0.79 % | NaN | NaN | NaN | NaN | NaN |
| 48 | ii(13), major | (2, 4, 6) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 49 | V7(964), major | (5, 1, 3, 4) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 50 | i(b13), major | (1, b3, 5) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 51 | ii2, major | (1, 2, 4, 6) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 52 | I(^2), major | (1, 2, 5) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 53 | iii64, major | (7, 3, 5) | 1.0 | 0.003929 | 0.39 % | NaN | NaN | NaN | NaN | NaN |
| 54 | ii64(9), major | (6, 2, 4) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
| 55 | iii, major | (3, 5, 7) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
| 56 | iii43/V, major | (#4, 6, 7, 2) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
| 57 | IV64(9), major | (1, 4, 6) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
| 58 | I64(9), major | (5, 1, 3) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
| 59 | vi, major | (6, 1, 3) | 0.5 | 0.001965 | 0.2 % | NaN | NaN | NaN | NaN | NaN |
unigram_proportions.plot_grouped()